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Journal Article

Complex fault system revealed by 3-D seismic reflection data with deep learning and fault network analysis

Authors
/persons/resource/wrona

Wrona,  Thilo
2.5 Geodynamic Modelling, 2.0 Geophysics, Departments, GFZ Publication Database, Deutsches GeoForschungsZentrum;

Pan,  Indranil
External Organizations;

Bell,  Rebecca E.
External Organizations;

Jackson,  Christopher A.-L.
External Organizations;

Gawthorpe,  Robert L.
External Organizations;

Fossen,  Haakon
External Organizations;

Osagiede,  Edoseghe E.
External Organizations;

/persons/resource/brune

Brune,  Sascha
2.5 Geodynamic Modelling, 2.0 Geophysics, Departments, GFZ Publication Database, Deutsches GeoForschungsZentrum;

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Fulltext (public)

5023652.pdf
(Publisher version), 20MB

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Citation

Wrona, T., Pan, I., Bell, R. E., Jackson, C.-A.-L., Gawthorpe, R. L., Fossen, H., Osagiede, E. E., Brune, S. (2023): Complex fault system revealed by 3-D seismic reflection data with deep learning and fault network analysis. - Solid Earth, 14, 11, 1181-1195.
https://doi.org/10.5194/se-14-1181-2023


Cite as: https://gfzpublic.gfz-potsdam.de/pubman/item/item_5023652
Abstract
Understanding where normal faults are located is critical for an accurate assessment of seismic hazard; the successful exploration for, and production of, natural (including low-carbon) resources; and the safe subsurface storage of CO2. Our current knowledge of normal fault systems is largely derived from seismic reflection data imaging, intracontinental rifts and continental margins. However, exploitation of these data sets is limited by interpretation biases, data coverage and resolution, restricting our understanding of fault systems. Applying supervised deep learning to one of the largest offshore 3-D seismic reflection data sets from the northern North Sea allows us to image the complexity of the rift-related fault system. The derived fault score volume allows us to extract almost 8000 individual normal faults of different geometries, which together form an intricate network characterised by a multitude of splays, junctions and intersections. Combining tools from deep learning, computer vision and network analysis allows us to map and analyse the fault system in great detail and in a fraction of the time required by conventional seismic interpretation methods. As such, this study shows how we can efficiently identify and analyse fault systems in increasingly large 3-D seismic data sets.